• Medientyp: E-Book
  • Titel: Energy Efficiency Can Deliver for Climate Policy : Evidence from Machine Learning-Based Targeting
  • Beteiligte: Christensen, Peter [VerfasserIn]; Francisco, Paul [VerfasserIn]; Myers, Erica [VerfasserIn]; Shao, Hansen [VerfasserIn]; Souza, Mateus [VerfasserIn]
  • Körperschaft: National Bureau of Economic Research
  • Erschienen: Cambridge, Mass: National Bureau of Economic Research, September 2022
  • Erschienen in: NBER working paper series ; no. w30467
  • Umfang: 1 Online-Ressource; illustrations (black and white)
  • Sprache: Englisch
  • Reproduktionsnotiz: Hardcopy version available to institutional subscribers
  • Entstehung:
  • Schlagwörter: Energieeinsparung ; Energiepolitik ; Klimaschutz ; USA ; General ; Energy ; Arbeitspapier ; Graue Literatur
  • Beschreibung: Building energy efficiency has been a cornerstone of greenhouse gas mitigation strategies for decades. However, impact evaluations have revealed that energy savings typically fall short of engineering model forecasts that currently guide funding decisions. This creates a resource allocation problem that impedes progress on climate change. Using data from the largest U.S. energy efficiency program, we demonstrate that a data-driven approach to predicting retrofit impacts based on previously realized outcomes is more accurate than the status quo engineering models. Targeting high-return interventions based on these predictions dramatically increases net social benefits, from $0.93 to $1.23 per dollar invested
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